With the constant development of network technologies, video on demand (VOD), IPTV and videophone services that are developed on the basis of network videos have become the main services on broadband networks. Thus, the quality of service (QoS) of the network videos may affect the development of such services directly.
To guarantee the QoS of network videos, it is necessary to perform quality evaluation and monitoring on the network videos, so that related adjustment and maintenance measures can be taken immediately to guarantee the normal operations of the preceding services.
In the prior art, a neural network is used to evaluate the quality of network videos according to parameters such as video rate, video frame rate, packet loss ratio, and number of intra-frame coded macro blocks.
In the prior art, the impacts of the video rate and packet loss ratio on the video quality are analyzed from the perspective of statistics. Nevertheless, a statistics method may obtain an inaccurate video rate and packet loss ratio, and thus the video quality cannot be evaluated accurately. Therefore, only overall sequence rating can be given, and no immediate quality of video frames is provided.